The Sum of Squared Differences (SSD) is a way of determining the correlation between two image regions. It is usually involved when motion compensation needs to be done. SSD is defined as followed:

$$!\sum\limits_{i,j\in W}(Image_1(i,j) - Image_2(x+i, y+j))^2$$

There are some variations of SSD. Like the Zero-mean Sum of Squared Differences (ZSSD):

$$!\sum\limits_{i,j\in W}(I_1(i,j) - \overline{I}_1(i,j) - I_2(x+i, y+j) + \overline{I}_2(x+i, y+j) )^2$$

See also:

• http://siddhantahuja.wordpress.com/2010/04/11/correlation-based-similarity-measures-summary